Mixed-initiative learning integrates complementary human and automated reasoning, taking advantage of their respective reasoning styles and computational strengths in order to solve complex learning problems. Mixed-initiative learning is at the basis of the Disciple approach for developing intelligent agents where a subject matter expert teaches an agent how to perform complex problem solving tasks and the agent learns from the expert, building and refining its knowledge base. Implementation of practical mixed-initiative learning systems, such as those from the Disciple family, requires advanced user-agent interactions to facilitate user-agent communication, the distribution of tasks between them, and the shift of initiative and control. This paper discusses some of these user-agent interaction issues in the context of the mixed-initiative rule learning method of the most recent version of the Disciple system.

This page is copyrighted by AAAI. All rights reserved. Your use of this site constitutes acceptance of all of AAAI's terms and conditions and privacy policy.